Argumentation-Based Probabilistic Causal Reasoning

We introduce an argumentation-based approach for conducting probabilistic causal reasoning. For that, we consider Pearl’s causal models where causal relations are modelled via structural equations and a probability distribution over background atoms. The probability that some causal statement holds is then computed by constructing a probabilistic argumentation framework and determining its extensions. This framework can then be used to generate argumentative explanations for the (non-)acceptance of the causal statement. Furthermore, we present an argumentation-based version of the twin network method for dealing with counterfactuals. Finally, we show that our approach yields the same results for causal and counterfactual queries as Pearl’s model.